Temporal network of information diffusion in Twitter

Millions of tweets, retweets and mentions are exchanged in Twitter everyday about very different subjects, events, opinions, etc. While aggregating this data over a time window might help to understand some properties of those processes in online social networks, the speed of information diffusion around particular time-bound events requires a temporal analysis of them. To show that (and with the help of the Text & Opinion Mining Group at IIC) we collected all tweets (750k) of the vibrant conversation around the disputed subject of the general strike of March 29th in Spain. The data spans 10 days from 03/27 to 04/04 and using the RTs related to the general strike between twitter accounts we build up the following temporal network of information diffusion in Twitter.

Day/night human rhythms are clearly seen, and there is an increase of activity in the evening/night before March 29th, which ended in the burst of RTs during that day. Moreover, using community-finding algorithms over the static (weighted) network of RTs we could assign each twitter account to one of the communities found. Analyzing the text of tweets within those communities we found the nature of the biggest groups: one is in favor of the economic motivations behind the strike, the other is not. Those communities fight close to dominate information propagation in Twitter even some days after the strike.

This video highlights the importance of temporal networks in the analysis of information diffusion in online social networks.

Technical details: the video was done using the amazing igraph package in R and encoded using ffmpeg. Thanks to everyone that contributes to those open-source projects for their work.

Edit (11/9/2012): I have post a tutorial on how to make this kind of visualizations here. Spread the word!

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12 Responses

  1. Brock says:

    Great post? Any chance you can share the code that put together the graph, identified the communities, and animated this over time? Would be a huge help for us learning to apply networks in other disciplines. Thanks in advance!

  2. admin says:

    Hi Brock. Thanks for your kind comment. I am preparing the corresponding “How I did it” post. It will be ready in a couple of days. Please stay tuned

  3. Erika says:

    Awesome work! Thanks for sharing. Looking forward to your “How I did it” post. 😉

  4. admin says:

    Thank you Erika. It’s taking me some more time than I thought. Stay tuned

  5. admin says:

    Ok. It is done! Find the details of how I made the animation in this other post http://estebanmoro.org/2012/11/temporal-networks-with-igraph-and-r-with-20-lines-of-code/
    Hope you like it. Comments are welcomed

  6. Which color is for and which one is against? 🙂

  7. admin says:

    Thanks Roger for your comment: orange is in favor and dark blue is against it.

  8. John says:

    Great post! Just a quick question if you don’t mind me asking.

    I assume nodes are users and edges are tweets. Tweets “disappear” at some point, what life-span did you assume for a tweet and how did you detrmine that?

  9. admin says:

    Thanks John!
    Edges in this example are RTs. Of course retweets are instantaneous but to get a more steady version of the networks we keep the link (RT) for half an hour after it is created.

  1. November 4, 2012

    […] verloor hij me, de meer data-wijze lezers kunnen erin duiken op zijn blog, en misschien is iemand zelfs zo vriendelijk om het in gewone mensentaal hieronder in de comments […]

  2. November 9, 2012

    […] Temporal network of information diffusion in Twitter | Implicit None. […]

  3. February 6, 2013

    […] Temporal network of information diffusion in Twitter | Implicit None […]

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